Importing Packages

#import the required libraries
import numpy as np 
import pandas as pd 
import seaborn as sns 
import matplotlib.ticker as mtick  
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.express as px
import plotly.graph_objs as go

from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier
from imblearn.combine import SMOTEENN

# Feature Processing (Scikit-learn processing, etc. )
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from collections import Counter
from imblearn.over_sampling import RandomOverSampler
import scipy.stats as stats
from scipy.stats import chi2_contingency

# Machine Learning (Scikit-learn Estimators, Catboost, LightGBM, etc. )
from sklearn.datasets import make_classification
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.linear_model import LogisticRegression, SGDClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_curve, auc, fbeta_score
from sklearn.metrics import confusion_matrix

# Hyperparameters Fine-tuning (Scikit-learn hp search, cross-validation, etc. )
from sklearn.model_selection import KFold, cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor

# Other packages
from tabulate import tabulate
import os, pickle

import warnings
warnings.filterwarnings('ignore')

import pickle

C:\Users\acer\anaconda3\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"

Load Data

voda = pd.read_csv('Telco-Customer-Churn.csv')

voda.sample(50)

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
4956	3217-FZDMN	Female	1	No	No	8	Yes	No	Fiber optic	No	...	Yes	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	94.45	742.95	Yes
6815	0270-THENM	Male	0	Yes	Yes	72	Yes	Yes	DSL	Yes	...	Yes	Yes	No	No	Two year	No	Bank transfer (automatic)	69.85	5102.35	No
3292	7284-BUYEC	Female	0	No	No	5	No	No phone service	DSL	No	...	No	No	Yes	Yes	Month-to-month	Yes	Credit card (automatic)	50.95	229.4	No
6625	3398-FSHON	Female	1	No	No	12	Yes	Yes	Fiber optic	No	...	No	No	Yes	No	Month-to-month	Yes	Electronic check	91.30	1094.5	Yes
1383	3334-CTHOL	Female	0	No	No	1	Yes	Yes	DSL	No	...	No	No	No	No	Month-to-month	Yes	Bank transfer (automatic)	49.95	49.95	Yes
5018	6928-ONTRW	Female	0	Yes	Yes	72	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Credit card (automatic)	19.70	1379.8	No
3410	4918-QLLIW	Male	0	No	No	3	Yes	No	DSL	No	...	No	No	Yes	No	Month-to-month	No	Credit card (automatic)	53.40	188.7	Yes
2377	9308-ANMVE	Male	0	No	Yes	47	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Electronic check	55.30	2654.05	No
1647	5442-XSDCW	Male	0	Yes	Yes	11	Yes	No	Fiber optic	No	...	No	No	Yes	No	Month-to-month	Yes	Bank transfer (automatic)	79.50	868.5	Yes
1934	4587-NUKOX	Female	0	No	No	3	Yes	No	Fiber optic	Yes	...	No	No	No	No	Month-to-month	Yes	Electronic check	79.10	246.5	Yes
4432	3891-NLXJB	Male	0	No	No	37	No	No phone service	DSL	Yes	...	No	Yes	No	No	Two year	Yes	Mailed check	40.55	1390.85	No
6263	8409-WQJUX	Female	0	No	No	25	No	No phone service	DSL	No	...	Yes	Yes	Yes	Yes	One year	No	Electronic check	54.20	1423.15	No
3059	7503-EPSZW	Female	0	Yes	Yes	64	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Mailed check	24.05	1559.15	No
5776	4291-TPNFG	Male	0	Yes	No	72	Yes	Yes	DSL	Yes	...	Yes	Yes	No	Yes	Two year	Yes	Bank transfer (automatic)	82.30	5980.55	No
468	8896-RAZCR	Female	0	No	Yes	44	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Mailed check	19.90	868.1	No
5799	8066-POXGX	Female	0	No	No	13	No	No phone service	DSL	No	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	35.10	446.1	Yes
69	7410-OIEDU	Male	0	No	No	10	Yes	No	Fiber optic	Yes	...	Yes	No	No	No	Month-to-month	Yes	Mailed check	79.85	887.35	No
280	0314-TKOSI	Female	0	No	No	6	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	No	Mailed check	55.15	322.9	No
2094	1057-FOGLZ	Female	0	No	No	18	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	19.65	391.7	No
1180	4835-YSJMR	Male	0	No	No	39	Yes	No	DSL	No	...	No	Yes	No	No	Two year	Yes	Bank transfer (automatic)	49.80	1971.15	No
1019	9391-TTOYH	Female	0	No	No	23	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Mailed check	19.50	470.2	No
4170	0906-QVPMS	Male	0	Yes	No	72	Yes	Yes	Fiber optic	Yes	...	Yes	Yes	Yes	Yes	Two year	No	Bank transfer (automatic)	115.15	8349.45	No
4447	7821-DPRQE	Male	0	Yes	No	68	Yes	Yes	Fiber optic	Yes	...	No	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	107.70	7320.9	No
2730	1169-SAOCL	Male	0	No	No	49	Yes	Yes	Fiber optic	No	...	No	Yes	Yes	Yes	One year	Yes	Bank transfer (automatic)	106.65	5168.1	No
3800	7973-DZRKH	Female	0	No	Yes	66	Yes	Yes	Fiber optic	Yes	...	Yes	Yes	No	No	Two year	Yes	Credit card (automatic)	90.95	5930.05	No
4485	7975-JMZNT	Male	0	Yes	No	66	Yes	Yes	DSL	Yes	...	Yes	Yes	Yes	Yes	Two year	No	Bank transfer (automatic)	91.70	6075.9	No
5500	7139-JZFVG	Male	0	Yes	Yes	60	Yes	No	DSL	Yes	...	Yes	No	No	No	Two year	No	Bank transfer (automatic)	60.50	3694.45	No
1961	4445-KWOKW	Female	0	No	No	42	Yes	Yes	DSL	Yes	...	No	No	No	No	One year	Yes	Bank transfer (automatic)	60.15	2421.6	No
4754	2072-ZVJJX	Male	0	Yes	No	68	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	25.25	1728.2	No
3526	9026-RNUJS	Male	1	No	No	5	Yes	No	DSL	No	...	Yes	No	No	No	Month-to-month	No	Electronic check	50.35	237.25	Yes
1605	7941-RCJOW	Male	0	No	No	65	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	Yes	Credit card (automatic)	19.55	1218.65	No
1174	6994-ORCWG	Female	0	No	No	14	Yes	Yes	DSL	No	...	No	No	No	No	One year	Yes	Mailed check	54.25	773.2	No
2372	0730-BGQGF	Male	0	Yes	Yes	71	Yes	Yes	DSL	Yes	...	Yes	Yes	Yes	Yes	Two year	No	Credit card (automatic)	90.30	6287.3	No
757	0030-FNXPP	Female	0	No	No	3	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	No	Mailed check	19.85	57.2	No
2514	8922-LIEGH	Female	1	No	No	25	Yes	Yes	Fiber optic	No	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	89.70	2187.55	Yes
4418	0378-XSZPU	Male	0	Yes	No	58	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Credit card (automatic)	60.30	3563.8	Yes
6422	8668-KNZTI	Male	0	No	No	52	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Electronic check	53.75	2790.65	No
5470	6726-NNFWD	Female	1	Yes	No	71	Yes	No	Fiber optic	No	...	Yes	No	No	Yes	Two year	No	Credit card (automatic)	89.45	6435.25	No
677	0822-GAVAP	Female	0	No	No	2	No	No phone service	DSL	No	...	No	No	No	Yes	Month-to-month	Yes	Electronic check	34.70	62.25	Yes
1226	7115-IRDHS	Female	0	Yes	No	72	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	24.65	1830.05	No
4666	8780-RSYYU	Female	0	No	No	25	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	Month-to-month	Yes	Credit card (automatic)	19.20	532.1	No
3046	8715-KKTFG	Female	0	Yes	No	61	Yes	Yes	Fiber optic	No	...	Yes	Yes	Yes	Yes	One year	Yes	Bank transfer (automatic)	103.30	6518.35	No
1412	7861-UVUFT	Female	0	Yes	No	15	Yes	No	Fiber optic	No	...	Yes	No	Yes	No	Month-to-month	Yes	Electronic check	84.30	1308.4	Yes
6272	4636-OLWOE	Male	0	No	Yes	54	Yes	No	DSL	No	...	Yes	Yes	No	No	One year	Yes	Electronic check	61.00	3283.05	No
4782	6175-IRFIT	Male	0	No	No	5	Yes	No	Fiber optic	No	...	No	No	No	Yes	Month-to-month	No	Mailed check	78.75	426.35	No
2262	4529-CKBCL	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	Yes	No	Month-to-month	Yes	Electronic check	80.20	146.05	Yes
4371	3372-CDXFJ	Male	0	Yes	Yes	13	Yes	Yes	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	One year	No	Bank transfer (automatic)	24.50	343.6	No
5194	1902-XBTFB	Male	0	No	Yes	22	Yes	No	Fiber optic	No	...	Yes	No	Yes	No	Month-to-month	Yes	Electronic check	89.40	2001.5	Yes
1012	7426-RHZGU	Male	0	No	No	9	Yes	No	Fiber optic	No	...	No	Yes	Yes	Yes	Month-to-month	Yes	Bank transfer (automatic)	95.90	827.45	No
174	5918-VUKWP	Female	0	No	No	32	Yes	No	No	No internet service	...	No internet service	No internet service	No internet service	No internet service	One year	No	Bank transfer (automatic)	20.55	654.55	No

50 rows × 21 columns

Data Understanding

This is a telecommunications company's customer dataset, containing various demographic and usage information for each customer, as well as whether or not they have churned (i.e. cancelled their service). Here are the meanings of the columns:

• customerID: unique identifier for each customer
• gender: customer gender (Male/Female)
• SeniorCitizen: indicates if the customer is a senior citizen or not (1, 0)
• Partner: indicates if the customer has a partner or not (Yes, No)
• Dependents: indicates if the customer has dependents or not (Yes, No)
• tenure: number of months the customer has stayed with the company
• PhoneService: indicates if the customer has a phone service or not (Yes, No)
• MultipleLines: indicates if the customer has multiple lines or not (Yes, No, No phone service)
• InternetService: type of internet service the customer has (DSL, Fiber optic, No)
• OnlineSecurity: indicates if the customer has online security or not (Yes, No, No internet service)
• DeviceProtection: indicates if the customer has device protection or not (Yes, No, No internet service)
• TechSupport: indicates if the customer has tech support or not (Yes, No, No internet service)
• StreamingTV: indicates if the customer has streaming TV or not (Yes, No, No internet service)
• StreamingMovies: indicates if the customer has streaming movies or not (Yes, No, No internet service)
• Contract: type of contract the customer has (Month-to-month, One year, Two year)
• PaperlessBilling: indicates if the customer has paperless billing or not (Yes, No)
• PaymentMethod: payment method used by the customer (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
• MonthlyCharges: amount charged to the customer monthly
• TotalCharges: total amount charged to the customer
• Churn: indicates if the customer has churned or not (Yes, No)

Exploratory Data Analysis

Data Overview

# Checking the data types of all the columns
voda.dtypes

customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges         object
Churn                object
dtype: object

# Check the descriptive statistics of numeric variables
voda.describe().T

	count	mean	std	min	25%	50%	75%	max
SeniorCitizen	7043.0	0.162147	0.368612	0.00	0.0	0.00	0.00	1.00
tenure	7043.0	32.371149	24.559481	0.00	9.0	29.00	55.00	72.00
MonthlyCharges	7043.0	64.761692	30.090047	18.25	35.5	70.35	89.85	118.75

Comment

SeniorCitizen is actually a categorical hence the 25%-50%-75% distribution is not propoer

75% customers have tenure less than 55 months

Average Monthly charges are USD 64.76 whereas 25% customers pay more than USD 89.85 per month

voda.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 21 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   customerID        7043 non-null   object 
 1   gender            7043 non-null   object 
 2   SeniorCitizen     7043 non-null   int64  
 3   Partner           7043 non-null   object 
 4   Dependents        7043 non-null   object 
 5   tenure            7043 non-null   int64  
 6   PhoneService      7043 non-null   object 
 7   MultipleLines     7043 non-null   object 
 8   InternetService   7043 non-null   object 
 9   OnlineSecurity    7043 non-null   object 
 10  OnlineBackup      7043 non-null   object 
 11  DeviceProtection  7043 non-null   object 
 12  TechSupport       7043 non-null   object 
 13  StreamingTV       7043 non-null   object 
 14  StreamingMovies   7043 non-null   object 
 15  Contract          7043 non-null   object 
 16  PaperlessBilling  7043 non-null   object 
 17  PaymentMethod     7043 non-null   object 
 18  MonthlyCharges    7043 non-null   float64
 19  TotalCharges      7043 non-null   object 
 20  Churn             7043 non-null   object 
dtypes: float64(1), int64(2), object(18)
memory usage: 1.1+ MB

Issues with Dataset:

• Datatype issues : Total charges should be in float dtype
• No missing data
• No duplicates
• The tenure columns needs to be categorized
• There are eleven blank values in the total charges column
  

Approach:

• Change the datatype of the TotalCharges column to numeric.
• Fill the null values with '0' since those columns correspond to customers that have not began their sbscription with the telco company.
• Binning the tenure columns
• Encoding the Churn column into 1's and 0's

Data Cleaning

#Create a copy of the original data
data = voda.copy() 

Cleaning TotalCharges Column

#Typecast TotalCharges column to numeric
data.TotalCharges = pd.to_numeric(data.TotalCharges, errors='coerce')

#Checking number of missing values
data.isnull().sum()

customerID           0
gender               0
SeniorCitizen        0
Partner              0
Dependents           0
tenure               0
PhoneService         0
MultipleLines        0
InternetService      0
OnlineSecurity       0
OnlineBackup         0
DeviceProtection     0
TechSupport          0
StreamingTV          0
StreamingMovies      0
Contract             0
PaperlessBilling     0
PaymentMethod        0
MonthlyCharges       0
TotalCharges        11
Churn                0
dtype: int64

#Fillin with 0s
data['TotalCharges'] = data['TotalCharges'].fillna(0)

Creating bins for tenure column

# Create new bin labels
labels = ["1-12", "13-24", "25-36","37-48", "49-60", "61-72"]

# Group tenure into new bins and update column
data['tenure_group'] = pd.cut(data.tenure, [1, 13, 25, 37, 49, 61, 73], right=False, labels=labels)

# Count values in new bins and sort by index
tenure_counts = data['tenure_group'].value_counts().sort_index()

Dropping Columns

#drop column customerID and tenure
data.drop(columns= ['customerID','tenure'], axis=1, inplace=True)

Questions:

1. What is the most preferred internet service of customers?

2. Understand customer demographics with respect to gender?

3. Which service is most popular with customers?

4. Are customers without dependents likely to have higher charges than those with dependent?

Hypothesis

Null Hypothesis : Senior citizen does not correlate with the tendency of customer churn

Alternate Hypothesis : Senior citizen correlate with the tendency of customer churn

Analysis

data.head()

	gender	SeniorCitizen	Partner	Dependents	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	tenure_group
0	Female	0	Yes	No	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No	1-12
1	Male	0	No	No	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	No	25-36
2	Male	0	No	No	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes	1-12
3	Male	0	No	No	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No	37-48
4	Female	0	No	No	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes	1-12

Univariate Analysis

Univariate Analysis

for i, predictor in enumerate(data.drop(columns=['Churn', 'TotalCharges', 'MonthlyCharges'])):
    fig = px.histogram(data, x=predictor, color='Churn', barmode='group',
                       color_discrete_sequence=['#1f77b4', '#aec7e8'],
                       title=f"Countplot of {predictor} by Churn")
    fig.update_layout(xaxis_title=predictor, yaxis_title="Count",
                      legend_title="Churn", height=400)
    fig.show()

Mth_Chgs = sns.kdeplot(data.MonthlyCharges[(data["Churn"] == 'Yes') ],
                color="Blue", fill = True, alpha=.5)
Mth_Chgs = sns.kdeplot(data.MonthlyCharges[(data["Churn"] == 'No') ],
                ax =Mth_Chgs, color="Green", fill= True, alpha=.5)

Mth_Chgs.legend(["Churn", "No Churn"],loc='upper right')
Mth_Chgs.set_ylabel('Density')
Mth_Chgs.set_xlabel('Monthly Charges')
Mth_Chgs.set_title('Monthly charges by churn')

plt.show()

tot_Chgs = sns.kdeplot(data.TotalCharges[(data["Churn"] == 'No') ],
                color="Green", fill=True, alpha=.5)
tot_Chgs = sns.kdeplot(data.TotalCharges[(data["Churn"] == 'Yes') ],
                ax =tot_Chgs, color="Blue", fill=True, alpha=.3)
tot_Chgs.legend(["No Churn","Churn"],loc='upper right')
tot_Chgs.set_ylabel('Density')
tot_Chgs.set_xlabel('Total Charges')
tot_Chgs.set_title('Total charges by churn');

fig = px.scatter(data_frame=data, x='MonthlyCharges', y='TotalCharges', 
                 trendline='ols', color='Churn', title='Monthly Charges vs Total Charges')

fig.update_layout(xaxis_title='Monthly Charges', yaxis_title='Total Charges',
                  margin=dict(l=50, r=50, t=50, b=50), height=400)

fig.show()

Derived Insight:

HIGH Churn seen in case of Month to month contracts, No online security, No Tech support, First year of subscription and Fibre Optics Internet

LOW Churn is seens in case of Long term contracts, Subscriptions without internet service and The customers engaged for 5+ years

Hypothesis Testing

Senior citizens and the tendecy to the customer churn

Null Hypothesis : Senior citizen does not correlate with the tendency of customer churn.
Alternate Hypothesis : Senior citizen correlate with the tendency of customer churn.

data['Churn'] = np.where(data.Churn == 'Yes',1,0)

from scipy.stats import chi2_contingency
Crosstabresults = pd.crosstab(index= voda['SeniorCitizen'], columns = voda['Churn'])
chisqresult  = chi2_contingency(Crosstabresults)

print('p-value: ', chisqresult[1])

p-value:  1.510066805092378e-36

Insights:
P-value is less than 0.05, which implies that we reject our null hypothesis. Senior citizen are more likely to churn.

Gender and the tendency to customer churn

Null Hypothesis :Gender does not correlate with the tendency of customer churn.
Alternate Hypothesis : Gender citizen correlate with the tendency of customer churn.

1. Convert all the categorical variables into dummy variables

Crosstabresults1 = pd.crosstab(index= voda['gender'], columns = voda['Churn'])
chisqresult1  = chi2_contingency(Crosstabresults1)

chisqresult1[1]

0.48657873605618596

1. Relationship between Monthly Charges and Total Charges

Insights:
P-value is greater than 0.05, which implies that we accept our null hypothesis. A customer's tendency to churn soes not depend on their gender.

1. What is the most preferred internet service of customers?

#Aggregating count of customers over the internet service columns
inter_serv = voda.groupby('InternetService')['customerID'].count().reset_index()
inter_serv.rename(columns={'customerID':'Number'}, inplace =True)
inter_serv

	InternetService	Number
0	DSL	2421
1	Fiber optic	3096
2	No	1526

data_inter_serv = inter_serv['Number']
keys = inter_serv['InternetService']
colors = px.colors.qualitative.Set1
explode = [0, 0.09, 0]

fig = go.Figure(data=[go.Pie(labels=keys, values=data_inter_serv, pull=[0.1, 0, 0], 
                             textinfo='label+percent', marker=dict(colors=colors))])
fig.update_layout(title='Customer Internet Preference')
fig.show()

2. Understand customer demographics with respect to gender

#Splitting Data into customer demographic columns
cus_demo = voda.loc[:,'customerID':'Dependents']
cus_demo.rename(columns = {'customerID': 'Number'},inplace = True)
cus_demo

	Number	gender	SeniorCitizen	Partner	Dependents
0	7590-VHVEG	Female	0	Yes	No
1	5575-GNVDE	Male	0	No	No
2	3668-QPYBK	Male	0	No	No
3	7795-CFOCW	Male	0	No	No
4	9237-HQITU	Female	0	No	No
...	...	...	...	...	...
7038	6840-RESVB	Male	0	Yes	Yes
7039	2234-XADUH	Female	0	Yes	Yes
7040	4801-JZAZL	Female	0	Yes	Yes
7041	8361-LTMKD	Male	1	Yes	No
7042	3186-AJIEK	Male	0	No	No

7043 rows × 5 columns

#Aggregate number of customers over demographic columns: Senior Citizen, Partner and Dependents
cus_demo1 = cus_demo.groupby(['SeniorCitizen','gender'])['Number'].count().reset_index()
cus_demo2 = cus_demo.groupby(['Partner', 'gender'])['Number'].count().reset_index()
cus_demo3 = cus_demo.groupby(['Dependents', 'gender'])['Number'].count().reset_index()

fig1 = px.bar(cus_demo1, x='SeniorCitizen', y='Number', color='gender', barmode='group',
              category_orders={'SeniorCitizen': [0, 1]}, color_discrete_sequence=px.colors.qualitative.Pastel1)

fig2 = px.bar(cus_demo2, x='Partner', y='Number', color='gender', barmode='group',
              category_orders={'Partner': ['Yes', 'No']}, color_discrete_sequence=px.colors.qualitative.Pastel1)

fig3 = px.bar(cus_demo3, x='Dependents', y='Number', color='gender', barmode='group',
              category_orders={'Dependents': ['Yes', 'No']}, color_discrete_sequence=px.colors.qualitative.Pastel1)

fig1.update_layout(title='Customer Demographics', xaxis_title='Senior Citizen', yaxis_title='Number')
fig2.update_layout(xaxis_title='Partner', yaxis_title='Number')
fig3.update_layout(xaxis_title='Dependents', yaxis_title='Number')

fig1.show()
fig2.show()
fig3.show()

Insights:

• Majority of customers are not senior citizens. That is , the client base is largely young people.
• Majority of the customers are not married.
• Majority of the customers do not have dependents.

3. Which service is most popular with customers?

#slicing vodafone services from the original data
cus_serv = data.loc[:,'PhoneService':'StreamingMovies']

#Aggregating the count over the columns
serv_tally = cus_serv[cus_serv[['PhoneService','MultipleLines','OnlineSecurity','OnlineBackup','DeviceProtection','TechSupport','StreamingTV','StreamingMovies']]=='Yes'].count()

serv_tally['InternetService'] = inter_serv['Number'].loc[0:1].sum()

# Sorting in ascending order
serv_tally.sort_values(ascending=True, inplace=True)

# Creating a horizontal bar chart
fig = go.Figure(go.Bar(
    x=serv_tally.values,
    y=serv_tally.index,
    orientation='h',
    marker_color='blue' # Setting color of the bars to blue
))

fig.update_layout(title='Most Popular Service', xaxis_title='Number of Customers')
fig.show()

4. Are customers without dependents likely to have higher charges than those with dependent?

fig = px.box(data_frame=data, x='Dependents', y='MonthlyCharges', color='Dependents',
             color_discrete_sequence=['#1f77b4', '#ff7f0e'])
fig.update_layout(title='Monthly Charges by Dependents',
                  xaxis_title='Dependents', yaxis_title='Monthly Charges')
fig.show()

Feature Processing - ML

data.head()

	gender	SeniorCitizen	Partner	Dependents	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn	tenure_group
0	Female	0	Yes	No	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	0	1-12
1	Male	0	No	No	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.50	0	25-36
2	Male	0	No	No	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	1	1-12
3	Male	0	No	No	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	0	37-48
4	Female	0	No	No	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	1	1-12

y = data['Churn']
X = data.drop('Churn', axis =1)

Feature Scaling

# initialize MinMaxScaler object with specified parameters
scaler = MinMaxScaler(feature_range=(0, 1), copy=True)

# select numerical columns to scale
num_cols = X.select_dtypes(include='number').columns

# scale numerical columns using MinMaxScaler
X[num_cols] = scaler.fit_transform(X[num_cols])

Feature Encoding

X = pd.get_dummies(X)
y = LabelEncoder().fit_transform(y)

Data Splitting

x_train,x_eval,y_train,y_eval=train_test_split(X,y,test_size=0.2)

Balancing Dataset

Approach:

• In order to balance the dataset, we can either oversample(increase the minority class) or undersample(decreasing the majority class)
• With regards to our dataset, it would be best to oversample since undersampling has tendency to remove our Not Churn data from the set.

sm = SMOTEENN()
X_resampled, y_resampled = sm.fit_resample(x_train,y_train)

Modelling

The following models would be used :

• Decision Tree Classifier
• Random Forest
• Gradient Boosting Classifier
• Logistic Regression

1. Decision Tree Classifier - Without balancing

tree_clf = DecisionTreeClassifier(criterion = "gini",random_state = 100,max_depth=6, min_samples_leaf=8)

tree_clf.fit(x_train,y_train)

DecisionTreeClassifier(max_depth=6, min_samples_leaf=8, random_state=100)

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Decision Tree Predictions

tree_clf_pred= tree_clf.predict(x_eval)

Decision Tree Classification Report

print(classification_report(y_eval, tree_clf_pred, labels=[0,1]))

              precision    recall  f1-score   support

           0       0.84      0.89      0.87      1038
           1       0.64      0.52      0.57       371

    accuracy                           0.80      1409
   macro avg       0.74      0.71      0.72      1409
weighted avg       0.79      0.80      0.79      1409

As you can see that the accuracy is quite low, and as it's an imbalanced dataset, we shouldn't consider Accuracy as our metrics to measure the model..

Hence, we need to check recall, precision & f1 score for the minority class, and it's quite evident that the precision, recall & f1 score is too low for Class 1, i.e. churned customers.

Decision Tree Classifier with balancing

tree_clf_bal = DecisionTreeClassifier(criterion = "gini",random_state = 100,max_depth=6, min_samples_leaf=8)

tree_clf_bal.fit(X_resampled,y_resampled)
tree_bal_pred = tree_clf_bal.predict(x_eval)

print(metrics.classification_report(y_eval,tree_bal_pred))

              precision    recall  f1-score   support

           0       0.94      0.61      0.74      1038
           1       0.45      0.89      0.60       371

    accuracy                           0.68      1409
   macro avg       0.69      0.75      0.67      1409
weighted avg       0.81      0.68      0.70      1409

print(metrics.confusion_matrix(y_eval, tree_bal_pred))

[[632 406]
 [ 41 330]]

Now we can see quite better results, i.e. Accuracy: 62%, and a very good recall, precision & f1 score for minority class. Let's try with some other classifier.

2. Random Forest Classifier - Without Balancing

forest_clf = RandomForestClassifier(n_estimators=100, criterion='gini', random_state = 100,max_depth=6, min_samples_leaf=8)

forest_clf.fit(x_train,y_train)

RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=100)

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forest_clf_pred = forest_clf.predict(x_eval)

print(classification_report(y_eval, forest_clf_pred, labels=[0,1]))

              precision    recall  f1-score   support

           0       0.82      0.92      0.87      1038
           1       0.67      0.45      0.54       371

    accuracy                           0.80      1409
   macro avg       0.75      0.68      0.70      1409
weighted avg       0.78      0.80      0.78      1409

Comment:

Good enough, however lets check with the balance dataset

2. Random Forest Classifier - With Balancing

forest_clf_bal=RandomForestClassifier(n_estimators=100, criterion='gini', random_state = 100,max_depth=6, min_samples_leaf=8)

forest_clf_bal.fit(X_resampled,y_resampled)

RandomForestClassifier(max_depth=6, min_samples_leaf=8, random_state=100)

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forest_bal_pred = forest_clf_bal.predict(x_eval)

print(metrics.classification_report(y_eval, forest_bal_pred))

              precision    recall  f1-score   support

           0       0.93      0.63      0.75      1038
           1       0.45      0.87      0.60       371

    accuracy                           0.69      1409
   macro avg       0.69      0.75      0.67      1409
weighted avg       0.80      0.69      0.71      1409

print(metrics.confusion_matrix(y_eval, forest_bal_pred))

[[650 388]
 [ 49 322]]

Comment:

After balancing, the f1 score has marginally improved at the expense of accuracy. This is because prior balancing, the machine was more biased towards the majority class

3. Gradient Boosting Classifier Model _without balancing

gb_clf = GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8)

gb_clf.fit(x_train, y_train)

GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)

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gb_clf_pred = gb_clf.predict(x_eval)

print(classification_report(y_eval, gb_clf_pred, labels=[0,1]))

              precision    recall  f1-score   support

           0       0.84      0.89      0.86      1038
           1       0.63      0.51      0.56       371

    accuracy                           0.79      1409
   macro avg       0.73      0.70      0.71      1409
weighted avg       0.78      0.79      0.79      1409

Gradient Boosting Classifier with balancing

gb_clf_bal = GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8)

gb_clf_bal.fit(X_resampled,y_resampled)

GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100)

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gb_bal_pred= gb_clf_bal.predict(x_eval)

print(metrics.confusion_matrix(y_eval, gb_bal_pred))

[[727 311]
 [ 63 308]]

print(metrics.classification_report(y_eval, gb_bal_pred))

              precision    recall  f1-score   support

           0       0.92      0.70      0.80      1038
           1       0.50      0.83      0.62       371

    accuracy                           0.73      1409
   macro avg       0.71      0.77      0.71      1409
weighted avg       0.81      0.73      0.75      1409

Comment:

Yes, a far better result with the Gradient Boosting Model on the balanced dataset.. we can still check for more classifiers

4. Logistic Regression - without balancing

lr = LogisticRegression(C=1.0, random_state=101)

lr.fit(x_train, y_train)

LogisticRegression(random_state=101)

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# Make predictions on the test data using the fitted model
lr_pred = lr.predict(x_eval)

print(classification_report(y_eval, lr_pred, labels=[0,1]))

              precision    recall  f1-score   support

           0       0.83      0.91      0.87      1038
           1       0.66      0.49      0.56       371

    accuracy                           0.80      1409
   macro avg       0.75      0.70      0.72      1409
weighted avg       0.79      0.80      0.79      1409

Logistic Regression - with balancing

lr_bal = LogisticRegression(C=1.0, random_state=101)

lr_bal.fit(X_resampled,y_resampled)

LogisticRegression(random_state=101)

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lr_bal_pred = lr_bal.predict(x_eval)

print(metrics.confusion_matrix(y_eval, lr_bal_pred))

[[736 302]
 [ 59 312]]

print(metrics.classification_report(y_eval, lr_bal_pred))

              precision    recall  f1-score   support

           0       0.93      0.71      0.80      1038
           1       0.51      0.84      0.63       371

    accuracy                           0.74      1409
   macro avg       0.72      0.78      0.72      1409
weighted avg       0.82      0.74      0.76      1409

Comment

We can see this also is far better: Now that we now the balanced data is better in results.

Model Evaluation

metrics_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])

models = [
    (DecisionTreeClassifier(criterion="gini", random_state=100, max_depth=6, min_samples_leaf=8), 'Decision Tree'),
    (RandomForestClassifier(n_estimators=100, criterion='gini', random_state=100, max_depth=6, min_samples_leaf=8), 'Random Forest Classifier'),
    (GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8), 'Gradient Boosting Classifier'),
    (LogisticRegression(C=1.0, random_state=100), 'Logistic Regression Model')
]

metrics_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])

for model, model_name in models:
    model.fit(x_train, y_train)
    y_pred = model.predict(x_eval)

    accuracy = accuracy_score(y_eval, y_pred)
    precision = precision_score(y_eval, y_pred)
    recall = recall_score(y_eval, y_pred)
    f1 = f1_score(y_eval, y_pred)

    metrics_df = metrics_df.append({'Model': model_name, 'Accuracy': accuracy,
                                    'Precision': precision, 'Recall': recall, 'F1 Score': f1}, ignore_index=True)
    
    metrics_df = metrics_df.sort_values(by='F1 Score', ascending=False)

# Print the final metrics dataframe
print(metrics_df)

                          Model  Accuracy  Precision    Recall  F1 Score
0                 Decision Tree  0.796309   0.639073  0.520216  0.573551
1  Gradient Boosting Classifier  0.792761   0.632107  0.509434  0.564179
3     Logistic Regression Model  0.799858   0.663004  0.487871  0.562112
2      Random Forest Classifier  0.796309   0.669355  0.447439  0.536349

metrics_df

	Model	Accuracy	Precision	Recall	F1 Score
0	Decision Tree	0.796309	0.639073	0.520216	0.573551
1	Gradient Boosting Classifier	0.792761	0.632107	0.509434	0.564179
3	Logistic Regression Model	0.799858	0.663004	0.487871	0.562112
2	Random Forest Classifier	0.796309	0.669355	0.447439	0.536349

# Create a list of models and their corresponding parameters
models = [
    (DecisionTreeClassifier(criterion="gini", random_state=100, max_depth=6, min_samples_leaf=8), 'Decision Tree'),
    (RandomForestClassifier(n_estimators=100, criterion='gini', random_state=100, max_depth=6, min_samples_leaf=8), 'Random Forest Classifier'),
    (GradientBoostingClassifier(criterion='friedman_mse', random_state=100, max_depth=6, min_samples_leaf=8), 'Gradient Boosting Classifier'),
    (LogisticRegression(C=1.0, random_state=100), 'Logistic Regression Model')

]

metrics_bal_df = pd.DataFrame(columns=['Model', 'Accuracy', 'Precision', 'Recall', 'F1 Score'])

sm = SMOTEENN()
X_resampled, y_resampled = sm.fit_resample(x_train, y_train)


for model, model_name in models:
    model.fit(X_resampled, y_resampled)
    yr_predict = model.predict(x_eval)

    accuracy = accuracy_score(y_eval, yr_predict)
    precision = precision_score(y_eval, yr_predict)
    recall = recall_score(y_eval, yr_predict)
    f1 = f1_score(y_eval, yr_predict)

    metrics_bal_df =  metrics_bal_df.append({'Model': model_name, 'Accuracy': accuracy,
                                                'Precision': precision, 'Recall': recall, 'F1 Score': f1}, ignore_index=True)

# Sort the dataframe in descending order based on Accuracy
metrics_bal_df =  metrics_bal_df.sort_values(by='F1 Score', ascending=False)

# Print the final metrics dataframe
print( metrics_bal_df)

                          Model  Accuracy  Precision    Recall  F1 Score
3     Logistic Regression Model  0.743080   0.507293  0.843666  0.633603
2  Gradient Boosting Classifier  0.731725   0.494382  0.830189  0.619718
1      Random Forest Classifier  0.690561   0.454545  0.876011  0.598527
0                 Decision Tree  0.678495   0.442577  0.851752  0.582488

 metrics_bal_df

	Model	Accuracy	Precision	Recall	F1 Score
3	Logistic Regression Model	0.743080	0.507293	0.843666	0.633603
2	Gradient Boosting Classifier	0.731725	0.494382	0.830189	0.619718
1	Random Forest Classifier	0.690561	0.454545	0.876011	0.598527
0	Decision Tree	0.678495	0.442577	0.851752	0.582488

Comment:

From the two tables, it can be observed that:

• Balancing the target variables improved the performance of our models
• The two best performing models based on the F1 score are Gradient Boosting Classifier and Logistic Regression Classifier.

Hyperparameter Tuning

models = [ gb_clf_bal, lr_bal]

Tuning Gradient Boosting Classifier Model

models[0].get_params()

{'ccp_alpha': 0.0,
 'criterion': 'friedman_mse',
 'init': None,
 'learning_rate': 0.1,
 'loss': 'log_loss',
 'max_depth': 6,
 'max_features': None,
 'max_leaf_nodes': None,
 'min_impurity_decrease': 0.0,
 'min_samples_leaf': 8,
 'min_samples_split': 2,
 'min_weight_fraction_leaf': 0.0,
 'n_estimators': 100,
 'n_iter_no_change': None,
 'random_state': 100,
 'subsample': 1.0,
 'tol': 0.0001,
 'validation_fraction': 0.1,
 'verbose': 0,
 'warm_start': False}

gb_clf_params = { 'ccp_alpha': [0.0,0.1,0.2],
           'max_depth': [4,6,8],
           'min_samples_leaf': [8, 10, 12],
          'n_estimators': [100,1000]
}

searcher = GridSearchCV(estimator = gb_clf_bal,
                        param_grid = gb_clf_params,
                        scoring = ['accuracy','balanced_accuracy','f1','precision','recall','roc_auc'],
                        refit = 'balanced_accuracy',
                        cv = 5,
                        verbose = 3)

searcher.fit(X_resampled,y_resampled)

Fitting 5 folds for each of 54 candidates, totalling 270 fits
[CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.924) balanced_accuracy: (test=0.925) f1: (test=0.933) precision: (test=0.953) recall: (test=0.915) roc_auc: (test=0.980) total time=   1.8s
[CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.938) balanced_accuracy: (test=0.936) f1: (test=0.947) precision: (test=0.946) recall: (test=0.948) roc_auc: (test=0.985) total time=   1.7s
[CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.972) balanced_accuracy: (test=0.967) f1: (test=0.976) precision: (test=0.958) recall: (test=0.995) roc_auc: (test=0.997) total time=   1.8s
[CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.950) balanced_accuracy: (test=0.943) f1: (test=0.958) precision: (test=0.936) recall: (test=0.982) roc_auc: (test=0.993) total time=   1.8s
[CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.961) balanced_accuracy: (test=0.957) f1: (test=0.967) precision: (test=0.956) recall: (test=0.978) roc_auc: (test=0.992) total time=   1.8s
[CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.918) balanced_accuracy: (test=0.923) f1: (test=0.927) precision: (test=0.966) recall: (test=0.891) roc_auc: (test=0.983) total time=  18.2s
[CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.943) balanced_accuracy: (test=0.943) f1: (test=0.951) precision: (test=0.959) recall: (test=0.943) roc_auc: (test=0.986) total time=  17.5s
[CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.973) f1: (test=0.981) precision: (test=0.966) recall: (test=0.997) roc_auc: (test=0.998) total time=  17.6s
[CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.964) balanced_accuracy: (test=0.957) f1: (test=0.970) precision: (test=0.946) recall: (test=0.995) roc_auc: (test=0.995) total time=  17.5s
[CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.974) balanced_accuracy: (test=0.970) f1: (test=0.978) precision: (test=0.966) recall: (test=0.990) roc_auc: (test=0.996) total time=  17.1s
[CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.915) balanced_accuracy: (test=0.917) f1: (test=0.925) precision: (test=0.951) recall: (test=0.901) roc_auc: (test=0.980) total time=   1.7s
[CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.939) balanced_accuracy: (test=0.938) f1: (test=0.948) precision: (test=0.950) recall: (test=0.946) roc_auc: (test=0.986) total time=   1.7s
[CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.973) balanced_accuracy: (test=0.969) f1: (test=0.977) precision: (test=0.963) recall: (test=0.992) roc_auc: (test=0.998) total time=   1.8s
[CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.950) balanced_accuracy: (test=0.944) f1: (test=0.958) precision: (test=0.938) recall: (test=0.980) roc_auc: (test=0.993) total time=   2.0s
[CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.959) balanced_accuracy: (test=0.955) f1: (test=0.965) precision: (test=0.956) recall: (test=0.975) roc_auc: (test=0.992) total time=   1.8s
[CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.913) balanced_accuracy: (test=0.918) f1: (test=0.923) precision: (test=0.962) recall: (test=0.886) roc_auc: (test=0.983) total time=  21.5s
[CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.941) balanced_accuracy: (test=0.940) f1: (test=0.949) precision: (test=0.954) recall: (test=0.945) roc_auc: (test=0.988) total time=  17.5s
[CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.967) recall: (test=0.997) roc_auc: (test=0.998) total time=  17.7s
[CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.963) balanced_accuracy: (test=0.956) f1: (test=0.969) precision: (test=0.944) recall: (test=0.995) roc_auc: (test=0.995) total time=  17.4s
[CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.971) balanced_accuracy: (test=0.966) f1: (test=0.975) precision: (test=0.961) recall: (test=0.990) roc_auc: (test=0.996) total time=  17.9s
[CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.918) balanced_accuracy: (test=0.919) f1: (test=0.928) precision: (test=0.948) recall: (test=0.910) roc_auc: (test=0.979) total time=   2.0s
[CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.937) balanced_accuracy: (test=0.935) f1: (test=0.946) precision: (test=0.945) recall: (test=0.948) roc_auc: (test=0.986) total time=   1.9s
[CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.974) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.964) recall: (test=0.993) roc_auc: (test=0.997) total time=   1.9s
[CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.950) balanced_accuracy: (test=0.943) f1: (test=0.958) precision: (test=0.935) recall: (test=0.983) roc_auc: (test=0.993) total time=   1.8s
[CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.961) balanced_accuracy: (test=0.958) f1: (test=0.967) precision: (test=0.959) recall: (test=0.975) roc_auc: (test=0.993) total time=   1.7s
[CV 1/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.909) balanced_accuracy: (test=0.915) f1: (test=0.919) precision: (test=0.962) recall: (test=0.880) roc_auc: (test=0.983) total time=  17.4s
[CV 2/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.938) balanced_accuracy: (test=0.939) f1: (test=0.947) precision: (test=0.957) recall: (test=0.936) roc_auc: (test=0.988) total time=  18.7s
[CV 3/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.979) balanced_accuracy: (test=0.976) f1: (test=0.983) precision: (test=0.969) recall: (test=0.997) roc_auc: (test=0.998) total time=  17.4s
[CV 4/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.961) balanced_accuracy: (test=0.954) f1: (test=0.967) precision: (test=0.943) recall: (test=0.993) roc_auc: (test=0.996) total time=  17.4s
[CV 5/5] END ccp_alpha=0.0, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.971) balanced_accuracy: (test=0.966) f1: (test=0.975) precision: (test=0.960) recall: (test=0.992) roc_auc: (test=0.996) total time=  18.2s
[CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.930) balanced_accuracy: (test=0.932) f1: (test=0.939) precision: (test=0.958) recall: (test=0.921) roc_auc: (test=0.986) total time=   2.6s
[CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.946) balanced_accuracy: (test=0.945) f1: (test=0.954) precision: (test=0.955) recall: (test=0.953) roc_auc: (test=0.990) total time=   3.4s
[CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.967) recall: (test=0.997) roc_auc: (test=0.997) total time=   2.8s
[CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.960) balanced_accuracy: (test=0.953) f1: (test=0.967) precision: (test=0.941) recall: (test=0.993) roc_auc: (test=0.995) total time=   2.9s
[CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.968) balanced_accuracy: (test=0.964) f1: (test=0.973) precision: (test=0.962) recall: (test=0.983) roc_auc: (test=0.996) total time=   2.9s
[CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.926) balanced_accuracy: (test=0.932) f1: (test=0.935) precision: (test=0.971) recall: (test=0.901) roc_auc: (test=0.986) total time=  26.5s
[CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.952) f1: (test=0.959) precision: (test=0.966) recall: (test=0.951) roc_auc: (test=0.990) total time=  28.4s
[CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.982) balanced_accuracy: (test=0.979) f1: (test=0.985) precision: (test=0.971) recall: (test=1.000) roc_auc: (test=0.998) total time=  27.5s
[CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.967) balanced_accuracy: (test=0.961) f1: (test=0.972) precision: (test=0.950) recall: (test=0.995) roc_auc: (test=0.996) total time=  26.1s
[CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.968) recall: (test=0.997) roc_auc: (test=0.997) total time=  26.2s
[CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.932) balanced_accuracy: (test=0.935) f1: (test=0.941) precision: (test=0.963) recall: (test=0.920) roc_auc: (test=0.984) total time=   2.5s
[CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.942) balanced_accuracy: (test=0.941) f1: (test=0.950) precision: (test=0.953) recall: (test=0.948) roc_auc: (test=0.990) total time=   2.9s
[CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.981) balanced_accuracy: (test=0.978) f1: (test=0.984) precision: (test=0.971) recall: (test=0.998) roc_auc: (test=0.998) total time=   3.0s
[CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.958) balanced_accuracy: (test=0.951) f1: (test=0.965) precision: (test=0.940) recall: (test=0.992) roc_auc: (test=0.995) total time=   2.5s
[CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.971) balanced_accuracy: (test=0.968) f1: (test=0.975) precision: (test=0.966) recall: (test=0.985) roc_auc: (test=0.996) total time=   2.5s
[CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.925) balanced_accuracy: (test=0.931) f1: (test=0.934) precision: (test=0.969) recall: (test=0.901) roc_auc: (test=0.986) total time=  27.5s
[CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.950) balanced_accuracy: (test=0.950) f1: (test=0.957) precision: (test=0.966) recall: (test=0.948) roc_auc: (test=0.989) total time=  27.1s
[CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.984) balanced_accuracy: (test=0.981) f1: (test=0.987) precision: (test=0.974) recall: (test=1.000) roc_auc: (test=0.998) total time=  26.3s
[CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.967) balanced_accuracy: (test=0.961) f1: (test=0.972) precision: (test=0.950) recall: (test=0.995) roc_auc: (test=0.995) total time=  26.2s
[CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.975) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.964) recall: (test=0.995) roc_auc: (test=0.996) total time=  27.0s
[CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.930) balanced_accuracy: (test=0.934) f1: (test=0.939) precision: (test=0.966) recall: (test=0.913) roc_auc: (test=0.985) total time=   3.0s
[CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.947) balanced_accuracy: (test=0.946) f1: (test=0.955) precision: (test=0.959) recall: (test=0.950) roc_auc: (test=0.990) total time=   2.8s
[CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.980) balanced_accuracy: (test=0.977) f1: (test=0.983) precision: (test=0.969) recall: (test=0.998) roc_auc: (test=0.998) total time=   2.7s
[CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.960) balanced_accuracy: (test=0.953) f1: (test=0.967) precision: (test=0.943) recall: (test=0.992) roc_auc: (test=0.995) total time=   2.7s
[CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.966) balanced_accuracy: (test=0.962) f1: (test=0.971) precision: (test=0.961) recall: (test=0.982) roc_auc: (test=0.995) total time=   2.5s
[CV 1/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.926) balanced_accuracy: (test=0.931) f1: (test=0.935) precision: (test=0.968) recall: (test=0.905) roc_auc: (test=0.985) total time=  25.7s
[CV 2/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.948) balanced_accuracy: (test=0.948) f1: (test=0.955) precision: (test=0.963) recall: (test=0.948) roc_auc: (test=0.989) total time=  27.3s
[CV 3/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.983) balanced_accuracy: (test=0.980) f1: (test=0.986) precision: (test=0.972) recall: (test=1.000) roc_auc: (test=0.998) total time=  26.0s
[CV 4/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.969) balanced_accuracy: (test=0.963) f1: (test=0.974) precision: (test=0.952) recall: (test=0.997) roc_auc: (test=0.996) total time=  27.0s
[CV 5/5] END ccp_alpha=0.0, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.974) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.966) recall: (test=0.992) roc_auc: (test=0.996) total time=  25.6s
[CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.938) balanced_accuracy: (test=0.941) f1: (test=0.946) precision: (test=0.968) recall: (test=0.925) roc_auc: (test=0.987) total time=   3.8s
[CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.953) balanced_accuracy: (test=0.952) f1: (test=0.960) precision: (test=0.963) recall: (test=0.956) roc_auc: (test=0.991) total time=   4.1s
[CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.975) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.963) recall: (test=0.997) roc_auc: (test=0.998) total time=   3.5s
[CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.962) balanced_accuracy: (test=0.955) f1: (test=0.968) precision: (test=0.943) recall: (test=0.995) roc_auc: (test=0.996) total time=   3.5s
[CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.974) balanced_accuracy: (test=0.971) f1: (test=0.978) precision: (test=0.970) recall: (test=0.985) roc_auc: (test=0.995) total time=   3.7s
[CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.939) balanced_accuracy: (test=0.943) f1: (test=0.947) precision: (test=0.972) recall: (test=0.923) roc_auc: (test=0.987) total time=  30.8s
[CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.952) f1: (test=0.960) precision: (test=0.963) recall: (test=0.956) roc_auc: (test=0.991) total time=  28.1s
[CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.983) balanced_accuracy: (test=0.980) f1: (test=0.986) precision: (test=0.972) recall: (test=1.000) roc_auc: (test=0.999) total time=  28.7s
[CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.966) balanced_accuracy: (test=0.960) f1: (test=0.971) precision: (test=0.949) recall: (test=0.995) roc_auc: (test=0.996) total time=  30.3s
[CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.967) recall: (test=0.995) roc_auc: (test=0.996) total time=  28.2s
[CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.936) balanced_accuracy: (test=0.938) f1: (test=0.945) precision: (test=0.963) recall: (test=0.926) roc_auc: (test=0.988) total time=   3.4s
[CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.955) balanced_accuracy: (test=0.954) f1: (test=0.961) precision: (test=0.963) recall: (test=0.960) roc_auc: (test=0.991) total time=   3.4s
[CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.980) balanced_accuracy: (test=0.977) f1: (test=0.983) precision: (test=0.971) recall: (test=0.997) roc_auc: (test=0.998) total time=   3.7s
[CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.963) balanced_accuracy: (test=0.956) f1: (test=0.969) precision: (test=0.946) recall: (test=0.993) roc_auc: (test=0.996) total time=   3.7s
[CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.971) balanced_accuracy: (test=0.968) f1: (test=0.975) precision: (test=0.966) recall: (test=0.985) roc_auc: (test=0.996) total time=   3.4s
[CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.932) balanced_accuracy: (test=0.936) f1: (test=0.941) precision: (test=0.970) recall: (test=0.913) roc_auc: (test=0.986) total time=  34.2s
[CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.955) balanced_accuracy: (test=0.954) f1: (test=0.961) precision: (test=0.965) recall: (test=0.958) roc_auc: (test=0.990) total time=  34.1s
[CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.982) balanced_accuracy: (test=0.979) f1: (test=0.985) precision: (test=0.971) recall: (test=1.000) roc_auc: (test=0.998) total time=  33.7s
[CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.967) balanced_accuracy: (test=0.961) f1: (test=0.972) precision: (test=0.953) recall: (test=0.992) roc_auc: (test=0.995) total time=  34.1s
[CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.973) balanced_accuracy: (test=0.968) f1: (test=0.977) precision: (test=0.961) recall: (test=0.993) roc_auc: (test=0.996) total time=  34.4s
[CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.942) balanced_accuracy: (test=0.944) f1: (test=0.950) precision: (test=0.969) recall: (test=0.931) roc_auc: (test=0.988) total time=   3.6s
[CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.951) balanced_accuracy: (test=0.950) f1: (test=0.958) precision: (test=0.961) recall: (test=0.955) roc_auc: (test=0.991) total time=   3.5s
[CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.982) balanced_accuracy: (test=0.979) f1: (test=0.985) precision: (test=0.972) recall: (test=0.998) roc_auc: (test=0.998) total time=   3.3s
[CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.963) balanced_accuracy: (test=0.956) f1: (test=0.969) precision: (test=0.946) recall: (test=0.993) roc_auc: (test=0.996) total time=   3.6s
[CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.973) balanced_accuracy: (test=0.969) f1: (test=0.977) precision: (test=0.966) recall: (test=0.988) roc_auc: (test=0.996) total time=   3.3s
[CV 1/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.925) balanced_accuracy: (test=0.931) f1: (test=0.934) precision: (test=0.969) recall: (test=0.901) roc_auc: (test=0.986) total time=  33.2s
[CV 2/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.951) f1: (test=0.960) precision: (test=0.960) recall: (test=0.960) roc_auc: (test=0.990) total time=  33.9s
[CV 3/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.980) balanced_accuracy: (test=0.976) f1: (test=0.984) precision: (test=0.968) recall: (test=1.000) roc_auc: (test=0.998) total time=  34.6s
[CV 4/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.969) balanced_accuracy: (test=0.963) f1: (test=0.974) precision: (test=0.955) recall: (test=0.993) roc_auc: (test=0.996) total time=  33.8s
[CV 5/5] END ccp_alpha=0.0, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.975) balanced_accuracy: (test=0.971) f1: (test=0.979) precision: (test=0.964) recall: (test=0.995) roc_auc: (test=0.996) total time=  33.5s
[CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   1.7s
[CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   1.8s
[CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   1.5s
[CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   1.5s
[CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   1.5s
[CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  16.8s
[CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  16.7s
[CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  16.6s
[CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  16.2s
[CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  16.8s
[CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   1.6s
[CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   1.5s
[CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   1.6s
[CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.0s
[CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   1.6s
[CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  16.7s
[CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  16.8s
[CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  16.9s
[CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  16.1s
[CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  16.2s
[CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   1.5s
[CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   1.6s
[CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   1.6s
[CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   1.5s
[CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   1.5s
[CV 1/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  16.7s
[CV 2/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  17.2s
[CV 3/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  15.9s
[CV 4/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  16.7s
[CV 5/5] END ccp_alpha=0.1, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  16.8s
[CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.3s
[CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.2s
[CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   2.3s
[CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.4s
[CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   2.5s
[CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  23.3s
[CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  23.0s
[CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  23.5s
[CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  22.9s
[CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  24.0s
[CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.2s
[CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.2s
[CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   2.3s
[CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.1s
[CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   2.1s
[CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  23.9s
[CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  23.0s
[CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  22.6s
[CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  24.9s
[CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  23.6s
[CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.1s
[CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.2s
[CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   2.3s
[CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.2s
[CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   2.4s
[CV 1/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  22.2s
[CV 2/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  23.5s
[CV 3/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  22.7s
[CV 4/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  23.8s
[CV 5/5] END ccp_alpha=0.1, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  22.9s
[CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.6s
[CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.6s
[CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   2.7s
[CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.6s
[CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   3.0s
[CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  26.4s
[CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  32.0s
[CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  30.0s
[CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  29.1s
[CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  28.2s
[CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.6s
[CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.6s
[CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   2.6s
[CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.6s
[CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   3.0s
[CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  26.6s
[CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  28.9s
[CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  28.7s
[CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  28.0s
[CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  27.5s
[CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   3.0s
[CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=   2.6s
[CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=   3.1s
[CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=   2.8s
[CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=   2.8s
[CV 1/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  27.4s
[CV 2/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.846) total time=  35.6s
[CV 3/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.848) total time=  28.7s
[CV 4/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.821) total time=  29.1s
[CV 5/5] END ccp_alpha=0.1, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.834) total time=  28.2s
[CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.6s
[CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.6s
[CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.6s
[CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.8s
[CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.6s
[CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.6s
[CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  17.3s
[CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  18.2s
[CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.5s
[CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.6s
[CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.6s
[CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.7s
[CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.5s
[CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.5s
[CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.7s
[CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.7s
[CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.4s
[CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  18.3s
[CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.0s
[CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  17.3s
[CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.0s
[CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.6s
[CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.8s
[CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.7s
[CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   1.5s
[CV 1/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  17.8s
[CV 2/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.6s
[CV 3/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  15.9s
[CV 4/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  15.8s
[CV 5/5] END ccp_alpha=0.2, max_depth=4, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  16.3s
[CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.2s
[CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.3s
[CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.5s
[CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.1s
[CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.1s
[CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.3s
[CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.4s
[CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  24.3s
[CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  22.9s
[CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.3s
[CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.2s
[CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.7s
[CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.1s
[CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.3s
[CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  24.2s
[CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.0s
[CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.1s
[CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  24.1s
[CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.9s
[CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.7s
[CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.4s
[CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.2s
[CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.2s
[CV 1/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  24.5s
[CV 2/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  23.1s
[CV 3/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  22.6s
[CV 4/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  22.3s
[CV 5/5] END ccp_alpha=0.2, max_depth=6, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  22.2s
[CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   3.0s
[CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.7s
[CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.8s
[CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.8s
[CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  28.8s
[CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.3s
[CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  28.7s
[CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.8s
[CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=8, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  28.5s
[CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.8s
[CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.7s
[CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   3.0s
[CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  28.7s
[CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  26.9s
[CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.7s
[CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  29.3s
[CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=10, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.0s
[CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.9s
[CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.6s
[CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=   3.0s
[CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=100; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=   2.7s
[CV 1/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.6s
[CV 2/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.4s
[CV 3/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.4s
[CV 4/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.586) balanced_accuracy: (test=0.500) f1: (test=0.739) precision: (test=0.586) recall: (test=1.000) roc_auc: (test=0.500) total time=  28.1s
[CV 5/5] END ccp_alpha=0.2, max_depth=8, min_samples_leaf=12, n_estimators=1000; accuracy: (test=0.587) balanced_accuracy: (test=0.500) f1: (test=0.740) precision: (test=0.587) recall: (test=1.000) roc_auc: (test=0.500) total time=  27.9s

GridSearchCV(cv=5,
             estimator=GradientBoostingClassifier(max_depth=6,
                                                  min_samples_leaf=8,
                                                  random_state=100),
             param_grid={'ccp_alpha': [0.0, 0.1, 0.2], 'max_depth': [4, 6, 8],
                         'min_samples_leaf': [8, 10, 12],
                         'n_estimators': [100, 1000]},
             refit='balanced_accuracy',
             scoring=['accuracy', 'balanced_accuracy', 'f1', 'precision',
                      'recall', 'roc_auc'],
             verbose=3)

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gb_clf_tuned = searcher.best_estimator_

gb_clf_tuned

GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
                           random_state=100)

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models.append(gb_clf_tuned)

models

[GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100),
 LogisticRegression(random_state=101),
 GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
                            random_state=100)]

Tuning the Logistic Regression Model

models[1].get_params()

{'C': 1.0,
 'class_weight': None,
 'dual': False,
 'fit_intercept': True,
 'intercept_scaling': 1,
 'l1_ratio': None,
 'max_iter': 100,
 'multi_class': 'auto',
 'n_jobs': None,
 'penalty': 'l2',
 'random_state': 101,
 'solver': 'lbfgs',
 'tol': 0.0001,
 'verbose': 0,
 'warm_start': False}

lr_params = { 'C': [1.0,2.0,3.0],
           'max_iter': [1000,10000,100000],
           'intercept_scaling': [1, 2, 3,]
    
    
}

searcher_lr = GridSearchCV(estimator = lr_bal,
                             param_grid = lr_params,
                             scoring = ['accuracy','balanced_accuracy','f1','precision','recall','roc_auc'],
                             refit = 'balanced_accuracy',
                             cv = 5,
                             verbose = 3
    )

searcher_lr.fit(X_resampled, y_resampled)

Fitting 5 folds for each of 27 candidates, totalling 135 fits
[CV 1/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.2s
[CV 2/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.1s
[CV 4/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.0s
[CV 5/5] END C=1.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.1s
[CV 1/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.0s
[CV 2/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.1s
[CV 4/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.0s
[CV 5/5] END C=1.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.0s
[CV 1/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.0s
[CV 2/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.1s
[CV 4/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.0s
[CV 5/5] END C=1.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.0s
[CV 1/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.1s
[CV 2/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.1s
[CV 4/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.0s
[CV 5/5] END C=1.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.0s
[CV 1/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.0s
[CV 2/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.1s
[CV 4/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.1s
[CV 5/5] END C=1.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.1s
[CV 1/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.1s
[CV 2/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.2s
[CV 4/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.1s
[CV 5/5] END C=1.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.1s
[CV 1/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.1s
[CV 2/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.2s
[CV 4/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.1s
[CV 5/5] END C=1.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.1s
[CV 1/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.1s
[CV 2/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.1s
[CV 4/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.0s
[CV 5/5] END C=1.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.0s
[CV 1/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.875) balanced_accuracy: (test=0.884) f1: (test=0.887) precision: (test=0.947) recall: (test=0.834) roc_auc: (test=0.964) total time=   0.1s
[CV 2/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.916) f1: (test=0.927) precision: (test=0.941) recall: (test=0.913) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.976) balanced_accuracy: (test=0.973) f1: (test=0.980) precision: (test=0.967) recall: (test=0.993) roc_auc: (test=0.998) total time=   0.2s
[CV 4/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.947) balanced_accuracy: (test=0.940) f1: (test=0.956) precision: (test=0.933) recall: (test=0.980) roc_auc: (test=0.995) total time=   0.0s
[CV 5/5] END C=1.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.953) balanced_accuracy: (test=0.948) f1: (test=0.961) precision: (test=0.945) recall: (test=0.977) roc_auc: (test=0.995) total time=   0.0s
[CV 1/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.0s
[CV 5/5] END C=2.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.2s
[CV 3/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.2s
[CV 2/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.0s
[CV 5/5] END C=2.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.0s
[CV 4/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.873) balanced_accuracy: (test=0.883) f1: (test=0.884) precision: (test=0.952) recall: (test=0.824) roc_auc: (test=0.963) total time=   0.0s
[CV 2/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.916) balanced_accuracy: (test=0.917) f1: (test=0.927) precision: (test=0.944) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.0s
[CV 3/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.977) balanced_accuracy: (test=0.974) f1: (test=0.981) precision: (test=0.969) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.951) balanced_accuracy: (test=0.944) f1: (test=0.959) precision: (test=0.936) recall: (test=0.983) roc_auc: (test=0.996) total time=   0.1s
[CV 5/5] END C=2.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.958) balanced_accuracy: (test=0.953) f1: (test=0.965) precision: (test=0.950) recall: (test=0.980) roc_auc: (test=0.996) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.2s
[CV 4/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=1, max_iter=1000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=1, max_iter=10000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=1, max_iter=100000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=2, max_iter=1000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.2s
[CV 1/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.2s
[CV 2/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.2s
[CV 3/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.2s
[CV 4/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.2s
[CV 5/5] END C=3.0, intercept_scaling=2, max_iter=10000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.2s
[CV 1/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.3s
[CV 2/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.2s
[CV 3/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=2, max_iter=100000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=3, max_iter=1000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=3, max_iter=10000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s
[CV 1/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.869) balanced_accuracy: (test=0.880) f1: (test=0.879) precision: (test=0.953) recall: (test=0.816) roc_auc: (test=0.963) total time=   0.1s
[CV 2/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.917) balanced_accuracy: (test=0.918) f1: (test=0.927) precision: (test=0.946) recall: (test=0.910) roc_auc: (test=0.978) total time=   0.1s
[CV 3/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.978) balanced_accuracy: (test=0.975) f1: (test=0.982) precision: (test=0.971) recall: (test=0.993) roc_auc: (test=0.999) total time=   0.1s
[CV 4/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.952) balanced_accuracy: (test=0.945) f1: (test=0.960) precision: (test=0.936) recall: (test=0.985) roc_auc: (test=0.997) total time=   0.1s
[CV 5/5] END C=3.0, intercept_scaling=3, max_iter=100000; accuracy: (test=0.962) balanced_accuracy: (test=0.957) f1: (test=0.968) precision: (test=0.953) recall: (test=0.983) roc_auc: (test=0.997) total time=   0.1s

GridSearchCV(cv=5, estimator=LogisticRegression(random_state=101),
             param_grid={'C': [1.0, 2.0, 3.0], 'intercept_scaling': [1, 2, 3],
                         'max_iter': [1000, 10000, 100000]},
             refit='balanced_accuracy',
             scoring=['accuracy', 'balanced_accuracy', 'f1', 'precision',
                      'recall', 'roc_auc'],
             verbose=3)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

lr_tuned = searcher_lr.best_estimator_

models.append(lr_tuned)

models

[GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100),
 LogisticRegression(random_state=101),
 GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
                            random_state=100),
 LogisticRegression(C=3.0, max_iter=1000, random_state=101)]

Exporting Key Components

components = { 'scaler': scaler,
               'model': models
    }

components

{'scaler': MinMaxScaler(),
 'model': [GradientBoostingClassifier(max_depth=6, min_samples_leaf=8, random_state=100),
  LogisticRegression(random_state=101),
  GradientBoostingClassifier(max_depth=8, min_samples_leaf=8, n_estimators=1000,
                             random_state=100),
  LogisticRegression(C=3.0, max_iter=1000, random_state=101)]}

#Create Folder
!mkdir export

#Create a destination folder
destination = os.path.join('.','export')

#export
with open(os.path.join(destination,'ml.pkl'),'wb') as f:
    pickle.dump(components, f)

#requirements
!pip freeze requirement.txt

alabaster @ file:///home/ktietz/src/ci/alabaster_1611921544520/work

WARNING: Ignoring invalid distribution -tatsmodels (c:\users\acer\anaconda3\lib\site-packages)

anaconda-client @ file:///C:/ci/anaconda-client_1635342725944/work
anaconda-navigator==2.3.2
anaconda-project @ file:///tmp/build/80754af9/anaconda-project_1626085644852/work
anyio @ file:///C:/ci/anyio_1620153135622/work/dist
appdirs==1.4.4
argh==0.26.2
argon2-cffi @ file:///C:/ci/argon2-cffi_1613037869401/work
arrow @ file:///C:/ci/arrow_1617738834352/work
asn1crypto @ file:///tmp/build/80754af9/asn1crypto_1596577642040/work
astroid @ file:///C:/ci/astroid_1628063282661/work
astropy @ file:///C:/ci/astropy_1629829318700/work
async-generator @ file:///home/ktietz/src/ci/async_generator_1611927993394/work
atomicwrites==1.4.0
attrs @ file:///tmp/build/80754af9/attrs_1620827162558/work
Automat @ file:///tmp/build/80754af9/automat_1600298431173/work
autopep8 @ file:///tmp/build/80754af9/autopep8_1620866417880/work
Babel @ file:///tmp/build/80754af9/babel_1620871417480/work
backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
backports.functools-lru-cache @ file:///tmp/build/80754af9/backports.functools_lru_cache_1618170165463/work
backports.shutil-get-terminal-size @ file:///tmp/build/80754af9/backports.shutil_get_terminal_size_1608222128777/work
backports.tempfile @ file:///home/linux1/recipes/ci/backports.tempfile_1610991236607/work
backports.weakref==1.0.post1
bcrypt @ file:///C:/ci/bcrypt_1607022693089/work
beautifulsoup4 @ file:///tmp/build/80754af9/beautifulsoup4_1631874778482/work
binaryornot @ file:///tmp/build/80754af9/binaryornot_1617751525010/work
bitarray @ file:///C:/ci/bitarray_1629133068652/work
bkcharts==0.2
black==19.10b0
bleach @ file:///tmp/build/80754af9/bleach_1628110601003/work
bokeh @ file:///C:/ci/bokeh_1635306491714/work
boto==2.49.0
boto3 @ file:///C:/Windows/TEMP/abs_4009c406-44ba-4406-8996-204d9b11202flt4kglbk/croots/recipe/boto3_1657820114895/work
botocore @ file:///C:/b/abs_807k4c4xt1/croot/botocore_1667495381641/work
Bottleneck @ file:///C:/ci/bottleneck_1607557040328/work
brotlipy==0.7.0
cached-property @ file:///tmp/build/80754af9/cached-property_1600785575025/work
category-encoders==2.6.0
certifi @ file:///C:/b/abs_4a0polqwty/croot/certifi_1683875377622/work/certifi
cffi @ file:///C:/ci/cffi_1625831756778/work
chardet @ file:///C:/ci/chardet_1607706937985/work
charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work
click==8.0.3
cloudpickle @ file:///tmp/build/80754af9/cloudpickle_1632508026186/work
clyent==1.2.2
colorama @ file:///tmp/build/80754af9/colorama_1607707115595/work
colorcet @ file:///C:/b/abs_46vyu0rpdl/croot/colorcet_1668084513237/work
comtypes==1.1.10
conda==4.12.0
conda-build==3.21.6
conda-content-trust @ file:///tmp/build/80754af9/conda-content-trust_1617045594566/work
conda-pack @ file:///tmp/build/80754af9/conda-pack_1611163042455/work
conda-package-handling @ file:///C:/ci/conda-package-handling_1618262410900/work
conda-repo-cli @ file:///tmp/build/80754af9/conda-repo-cli_1620168426516/work
conda-token @ file:///tmp/build/80754af9/conda-token_1620076980546/work
conda-verify==3.4.2
constantly==15.1.0
contextlib2 @ file:///Users/ktietz/demo/mc3/conda-bld/contextlib2_1630668244042/work
conti==1.0
cookiecutter @ file:///tmp/build/80754af9/cookiecutter_1617748928239/work
cryptography @ file:///C:/ci/cryptography_1633520531101/work
cssselect==1.1.0
cycler==0.10.0
Cython @ file:///C:/ci/cython_1636018292912/work
cytoolz==0.11.0
daal4py==2021.3.0
dask==2021.10.0
datashader @ file:///C:/b/abs_cdf2s6zvt6/croot/datashader_1670841914509/work
datashape==0.5.4
debugpy @ file:///C:/ci/debugpy_1629222819322/work
decorator @ file:///tmp/build/80754af9/decorator_1632776554403/work
defusedxml @ file:///tmp/build/80754af9/defusedxml_1615228127516/work
diff-match-patch @ file:///Users/ktietz/demo/mc3/conda-bld/diff-match-patch_1630511840874/work
distributed @ file:///C:/ci/distributed_1635968318313/work
docutils @ file:///C:/ci/docutils_1620828264669/work
emoji==2.2.0
entrypoints==0.3
et-xmlfile==1.1.0
Faker==16.6.1
fastcache @ file:///C:/ci/fastcache_1607571310570/work
filelock @ file:///tmp/build/80754af9/filelock_1635402558181/work
flake8 @ file:///tmp/build/80754af9/flake8_1620776156532/work
Flask @ file:///home/ktietz/src/ci/flask_1611932660458/work
fonttools==4.25.0
fsspec @ file:///tmp/build/80754af9/fsspec_1636116461911/work
future @ file:///C:/ci/future_1607568713721/work
gensim @ file:///C:/ci/gensim_1613994497440/work
gevent @ file:///C:/ci/gevent_1628273776273/work
glob2 @ file:///home/linux1/recipes/ci/glob2_1610991677669/work
greenlet @ file:///C:/ci/greenlet_1628888275363/work
h5py @ file:///C:/ci/h5py_1622088609188/work
HeapDict @ file:///Users/ktietz/demo/mc3/conda-bld/heapdict_1630598515714/work
holoviews @ file:///opt/conda/conda-bld/holoviews_1645454331194/work
html5lib @ file:///Users/ktietz/demo/mc3/conda-bld/html5lib_1629144453894/work
hvplot @ file:///C:/b/abs_13un17_4x_/croot/hvplot_1670508919193/work
hyperlink @ file:///tmp/build/80754af9/hyperlink_1610130746837/work
idna @ file:///tmp/build/80754af9/idna_1622654382723/work
imagecodecs @ file:///C:/ci/imagecodecs_1635511087451/work
imageio @ file:///tmp/build/80754af9/imageio_1617700267927/work
imagesize @ file:///Users/ktietz/demo/mc3/conda-bld/imagesize_1628863108022/work
imbalanced-learn==0.10.1
imblearn==0.0
importlib-metadata @ file:///C:/ci/importlib-metadata_1631916826748/work
importlib-resources==5.10.2
incremental @ file:///tmp/build/80754af9/incremental_1636629750599/work
inflection==0.5.1
iniconfig @ file:///home/linux1/recipes/ci/iniconfig_1610983019677/work
intake @ file:///C:/b/abs_56qe6jc56w/croot/intake_1668765380923/work
intervaltree @ file:///Users/ktietz/demo/mc3/conda-bld/intervaltree_1630511889664/work
ipykernel @ file:///C:/ci/ipykernel_1633545585502/work/dist/ipykernel-6.4.1-py3-none-any.whl
ipython @ file:///C:/ci/ipython_1635944283918/work
ipython-genutils @ file:///tmp/build/80754af9/ipython_genutils_1606773439826/work
ipywidgets @ file:///tmp/build/80754af9/ipywidgets_1634143127070/work
isort @ file:///tmp/build/80754af9/isort_1628603791788/work
itemadapter @ file:///tmp/build/80754af9/itemadapter_1626442940632/work
itemloaders @ file:///opt/conda/conda-bld/itemloaders_1646805235997/work
itsdangerous @ file:///tmp/build/80754af9/itsdangerous_1621432558163/work
jdcal @ file:///Users/ktietz/demo/mc3/conda-bld/jdcal_1630584345063/work
jedi @ file:///C:/ci/jedi_1611341083684/work
Jinja2 @ file:///tmp/build/80754af9/jinja2_1612213139570/work
jinja2-time @ file:///tmp/build/80754af9/jinja2-time_1617751524098/work
jmespath @ file:///Users/ktietz/demo/mc3/conda-bld/jmespath_1630583964805/work
joblib==1.2.0
json5 @ file:///tmp/build/80754af9/json5_1624432770122/work
jsonschema @ file:///Users/ktietz/demo/mc3/conda-bld/jsonschema_1630511932244/work
jupyter @ file:///C:/ci/jupyter_1607685287094/work
jupyter-client @ file:///tmp/build/80754af9/jupyter_client_1616770841739/work
jupyter-console @ file:///tmp/build/80754af9/jupyter_console_1616615302928/work
jupyter-core @ file:///C:/ci/jupyter_core_1633420716440/work
jupyter-server @ file:///C:/ci/jupyter_server_1616084298419/work
jupyterlab @ file:///tmp/build/80754af9/jupyterlab_1635799997693/work
jupyterlab-pygments @ file:///tmp/build/80754af9/jupyterlab_pygments_1601490720602/work
jupyterlab-server @ file:///tmp/build/80754af9/jupyterlab_server_1633419203660/work
jupyterlab-widgets @ file:///tmp/build/80754af9/jupyterlab_widgets_1609884341231/work
keyring @ file:///C:/ci/keyring_1629321702436/work
kiwisolver @ file:///C:/ci/kiwisolver_1612282555033/work
lazy-object-proxy @ file:///C:/ci/lazy-object-proxy_1616529288960/work
libarchive-c @ file:///tmp/build/80754af9/python-libarchive-c_1617780486945/work
llvmlite==0.39.1
locket==0.2.1
lxml @ file:///C:/ci/lxml_1616443418777/work
Markdown @ file:///C:/b/abs_98lv_ucina/croot/markdown_1671541919225/work
MarkupSafe @ file:///C:/ci/markupsafe_1607027406824/work
matplotlib @ file:///C:/ci/matplotlib-suite_1634667159685/work
matplotlib-inline @ file:///tmp/build/80754af9/matplotlib-inline_1628242447089/work
mccabe==0.6.1
menuinst @ file:///C:/ci/menuinst_1631733438520/work
mistune @ file:///C:/ci/mistune_1607359457024/work
mkl-fft==1.3.1
mkl-random @ file:///C:/ci/mkl_random_1626186184308/work
mkl-service==2.4.0
mock @ file:///tmp/build/80754af9/mock_1607622725907/work
more-itertools @ file:///tmp/build/80754af9/more-itertools_1635423142362/work
mpmath==1.2.1
msgpack @ file:///C:/ci/msgpack-python_1612287350784/work
multipledispatch @ file:///C:/ci/multipledispatch_1607574329826/work
munkres==1.1.4
mypy-extensions==0.4.3
navigator-updater==0.2.1
nbclassic @ file:///tmp/build/80754af9/nbclassic_1616085367084/work
nbclient @ file:///tmp/build/80754af9/nbclient_1614364831625/work
nbconvert @ file:///C:/ci/nbconvert_1624479160025/work
nbformat @ file:///tmp/build/80754af9/nbformat_1617383369282/work
nest-asyncio @ file:///tmp/build/80754af9/nest-asyncio_1613680548246/work
networkx @ file:///tmp/build/80754af9/networkx_1633639043937/work
nltk==3.6.5
nose @ file:///tmp/build/80754af9/nose_1606773131901/work
notebook @ file:///C:/ci/notebook_1635393701545/work
numba @ file:///C:/b/abs_e53pp2e4k7/croot/numba_1670258349527/work
numexpr @ file:///C:/ci/numexpr_1618856728739/work
numpy @ file:///C:/b/abs_datssh7cer/croot/numpy_and_numpy_base_1672336199388/work
numpydoc @ file:///tmp/build/80754af9/numpydoc_1605117425582/work
nxtools==1.6
olefile @ file:///Users/ktietz/demo/mc3/conda-bld/olefile_1629805411829/work
openpyxl @ file:///tmp/build/80754af9/openpyxl_1632777717936/work
packaging==23.1
pandas @ file:///C:/b/abs_cdcgk91igc/croots/recipe/pandas_1663772960432/work
pandocfilters @ file:///C:/ci/pandocfilters_1605114832805/work
panel @ file:///C:/b/abs_37xf16wbwu/croot/panel_1674166324937/work
param @ file:///C:/b/abs_d799n8xz_7/croot/param_1671697759755/work
paramiko @ file:///tmp/build/80754af9/paramiko_1598886428689/work
parsel @ file:///C:/ci/parsel_1646740216444/work
parso @ file:///tmp/build/80754af9/parso_1617223946239/work
partd @ file:///tmp/build/80754af9/partd_1618000087440/work
path @ file:///C:/ci/path_1624287837534/work
pathlib2 @ file:///C:/ci/pathlib2_1625585796814/work
pathspec==0.7.0
patsy==0.5.2
pep8==1.7.1
pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work
pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
Pillow==8.4.0
pkginfo==1.7.1
plotly @ file:///C:/ci/plotly_1658142442431/work
pluggy @ file:///C:/ci/pluggy_1615976440052/work
ply==3.11
pmdarima==2.0.3
poyo @ file:///tmp/build/80754af9/poyo_1617751526755/work
prometheus-client @ file:///tmp/build/80754af9/prometheus_client_1623189609245/work
prompt-toolkit @ file:///tmp/build/80754af9/prompt-toolkit_1633440160888/work
Protego @ file:///tmp/build/80754af9/protego_1598657180827/work
psutil @ file:///C:/ci/psutil_1612298199233/work
ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
py @ file:///tmp/build/80754af9/py_1607971587848/work
pyasn1 @ file:///Users/ktietz/demo/mc3/conda-bld/pyasn1_1629708007385/work
pyasn1-modules==0.2.8
pycodestyle @ file:///tmp/build/80754af9/pycodestyle_1615748559966/work
pycosat==0.6.3
pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work
pyct @ file:///C:/ci/pyct_1658488033428/work
pycurl==7.44.1
PyDispatcher==2.0.5
pydocstyle @ file:///tmp/build/80754af9/pydocstyle_1621600989141/work
pyerfa @ file:///C:/ci/pyerfa_1621560974055/work
pyflakes @ file:///tmp/build/80754af9/pyflakes_1617200973297/work
Pygments @ file:///tmp/build/80754af9/pygments_1629234116488/work
PyHamcrest @ file:///tmp/build/80754af9/pyhamcrest_1615748656804/work
PyJWT @ file:///C:/ci/pyjwt_1619682721924/work
pylint @ file:///C:/ci/pylint_1627536884966/work
pyls-spyder==0.4.0
PyNaCl @ file:///C:/ci/pynacl_1607612759007/work
pyodbc===4.0.0-unsupported
pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1635333100036/work
pyparsing @ file:///tmp/build/80754af9/pyparsing_1635766073266/work
pyreadline==2.1
pyrsistent @ file:///C:/ci/pyrsistent_1636093225342/work
PySocks @ file:///C:/ci/pysocks_1605307512533/work
pytest==6.2.4
python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
python-lsp-black @ file:///tmp/build/80754af9/python-lsp-black_1634232156041/work
python-lsp-jsonrpc==1.0.0
python-lsp-server==1.2.4
python-slugify @ file:///tmp/build/80754af9/python-slugify_1620405669636/work
python-snappy @ file:///C:/ci/python-snappy_1610133405910/work
pytz==2021.3
pyviz-comms @ file:///tmp/build/80754af9/pyviz_comms_1623747165329/work
PyWavelets @ file:///C:/ci/pywavelets_1607645631519/work
pywin32==228
pywin32-ctypes @ file:///C:/ci/pywin32-ctypes_1607553594546/work
pywinpty @ file:///C:/ci/pywinpty_1607419945780/work
PyYAML==6.0
pyzmq @ file:///C:/ci/pyzmq_1628276105793/work
QDarkStyle @ file:///tmp/build/80754af9/qdarkstyle_1617386714626/work
qstylizer @ file:///tmp/build/80754af9/qstylizer_1617713584600/work/dist/qstylizer-0.1.10-py2.py3-none-any.whl
QtAwesome @ file:///tmp/build/80754af9/qtawesome_1615991616277/work
qtconsole @ file:///tmp/build/80754af9/qtconsole_1632739723211/work
QtPy @ file:///tmp/build/80754af9/qtpy_1629397026935/work
queuelib==1.5.0
regex @ file:///C:/ci/regex_1628063427816/work
requests @ file:///tmp/build/80754af9/requests_1629994808627/work
requests-file @ file:///Users/ktietz/demo/mc3/conda-bld/requests-file_1629455781986/work
rise @ file:///C:/ci/rise_1612306697775/work
rope @ file:///tmp/build/80754af9/rope_1623703006312/work
Rtree @ file:///C:/ci/rtree_1618421015405/work
ruamel-yaml-conda @ file:///C:/ci/ruamel_yaml_1616016898638/work
s3transfer @ file:///C:/ci/s3transfer_1654512518418/work
scikit-image==0.18.3
scikit-learn==1.2.1
scikit-learn-intelex==2021.20210714.120553
scipy @ file:///C:/ci/scipy_1630606917240/work
Scrapy @ file:///C:/ci/scrapy_1646837986255/work
seaborn @ file:///tmp/build/80754af9/seaborn_1629307859561/work
Send2Trash @ file:///tmp/build/80754af9/send2trash_1632406701022/work
service-identity @ file:///Users/ktietz/demo/mc3/conda-bld/service_identity_1629460757137/work
simplegeneric==0.8.1
singledispatch @ file:///tmp/build/80754af9/singledispatch_1629321204894/work
sip==4.19.13
six @ file:///tmp/build/80754af9/six_1623709665295/work
smart-open @ file:///C:/ci/smart_open_1651235069716/work
sniffio @ file:///C:/ci/sniffio_1614030527509/work
snowballstemmer @ file:///tmp/build/80754af9/snowballstemmer_1611258885636/work
sortedcollections @ file:///tmp/build/80754af9/sortedcollections_1611172717284/work
sortedcontainers @ file:///tmp/build/80754af9/sortedcontainers_1623949099177/work
soupsieve @ file:///tmp/build/80754af9/soupsieve_1616183228191/work
Sphinx==4.2.0
sphinxcontrib-applehelp @ file:///home/ktietz/src/ci/sphinxcontrib-applehelp_1611920841464/work
sphinxcontrib-devhelp @ file:///home/ktietz/src/ci/sphinxcontrib-devhelp_1611920923094/work
sphinxcontrib-htmlhelp @ file:///tmp/build/80754af9/sphinxcontrib-htmlhelp_1623945626792/work
sphinxcontrib-jsmath @ file:///home/ktietz/src/ci/sphinxcontrib-jsmath_1611920942228/work
sphinxcontrib-qthelp @ file:///home/ktietz/src/ci/sphinxcontrib-qthelp_1611921055322/work
sphinxcontrib-serializinghtml @ file:///tmp/build/80754af9/sphinxcontrib-serializinghtml_1624451540180/work
sphinxcontrib-websupport @ file:///tmp/build/80754af9/sphinxcontrib-websupport_1597081412696/work
spyder @ file:///C:/ci/spyder_1636480369575/work
spyder-kernels @ file:///C:/ci/spyder-kernels_1634237096710/work
SQLAlchemy @ file:///C:/ci/sqlalchemy_1626948551162/work
squarify==0.4.3
statsmodels @ file:///C:/b/abs_bdqo3zaryj/croot/statsmodels_1676646249859/work
sweetviz==2.1.4
sympy @ file:///C:/ci/sympy_1635219088507/work
tables==3.6.1
tabulate @ file:///C:/ci/tabulate_1657619055201/work
TBB==0.2
tblib @ file:///Users/ktietz/demo/mc3/conda-bld/tblib_1629402031467/work
tenacity @ file:///C:/Windows/TEMP/abs_980d07a6-8e21-4174-9c17-7296219678ads7dhdov_/croots/recipe/tenacity_1657899108023/work
terminado==0.9.4
testpath @ file:///tmp/build/80754af9/testpath_1624638946665/work
text-unidecode @ file:///Users/ktietz/demo/mc3/conda-bld/text-unidecode_1629401354553/work
textdistance @ file:///tmp/build/80754af9/textdistance_1612461398012/work
threadpoolctl @ file:///Users/ktietz/demo/mc3/conda-bld/threadpoolctl_1629802263681/work
three-merge @ file:///tmp/build/80754af9/three-merge_1607553261110/work
tifffile @ file:///tmp/build/80754af9/tifffile_1627275862826/work
tinycss @ file:///tmp/build/80754af9/tinycss_1617713798712/work
tldextract @ file:///opt/conda/conda-bld/tldextract_1646638314385/work
toml @ file:///tmp/build/80754af9/toml_1616166611790/work
toolz @ file:///home/linux1/recipes/ci/toolz_1610987900194/work
tornado @ file:///C:/ci/tornado_1606924294691/work
tqdm @ file:///tmp/build/80754af9/tqdm_1635330843403/work
traitlets @ file:///tmp/build/80754af9/traitlets_1632522747050/work
Twisted @ file:///C:/Windows/Temp/abs_ccblv2rzfa/croots/recipe/twisted_1659592764512/work
twisted-iocpsupport @ file:///C:/ci/twisted-iocpsupport_1646798932792/work
typed-ast @ file:///C:/ci/typed-ast_1624953797214/work
typing-extensions @ file:///tmp/build/80754af9/typing_extensions_1631814937681/work
ujson @ file:///C:/ci/ujson_1611259568517/work
unicodecsv==0.14.1
Unidecode @ file:///tmp/build/80754af9/unidecode_1614712377438/work
urllib3==1.26.7
w3lib @ file:///Users/ktietz/demo/mc3/conda-bld/w3lib_1629359764703/work
watchdog @ file:///C:/ci/watchdog_1624955113064/work
wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work
webencodings==0.5.1
Werkzeug @ file:///tmp/build/80754af9/werkzeug_1635505089296/work
whichcraft @ file:///tmp/build/80754af9/whichcraft_1617751293875/work
widgetsnbextension @ file:///C:/ci/widgetsnbextension_1607531582688/work
win-inet-pton @ file:///C:/ci/win_inet_pton_1605306162074/work
win-unicode-console==0.5
wincertstore==0.2
wordcloud==1.8.2.2
wrapt @ file:///C:/ci/wrapt_1607574570428/work
xarray @ file:///C:/b/abs_2fi_umrauo/croot/xarray_1668776806973/work
xgboost==1.7.5
xlrd @ file:///tmp/build/80754af9/xlrd_1608072521494/work
XlsxWriter @ file:///tmp/build/80754af9/xlsxwriter_1628603415431/work
xlwings==0.24.9
xlwt==1.3.0
xmltodict @ file:///Users/ktietz/demo/mc3/conda-bld/xmltodict_1629301980723/work
yapf @ file:///tmp/build/80754af9/yapf_1615749224965/work
zict==2.0.0
zipp @ file:///tmp/build/80754af9/zipp_1633618647012/work
zope.event==4.5.0
zope.interface @ file:///C:/ci/zope.interface_1625036252485/work

#save file in export
!pip freeze > export/requirement.txt

WARNING: Ignoring invalid distribution -tatsmodels (c:\users\acer\anaconda3\lib\site-packages)